Traditional algorithms for dynamic OFDMA resource allocation have relatively deterministic system capacity and user fairness. Thus, in this letter, an efficient scheme is proposed to flexibly adjust quality-of-service for users, which is achieved by appropriately setting minimum data-rate of each user.

In this paper, we analyzed the performance of dynamic resource allocation with channel de-allocation and buffering in cellular networks. Buffers are applied for data traffic to reduce the packet loss probability while channel de-allocation is exploited to reduce the voice blocking probability. The results show that while buffering data traffic can reduce the packet loss probability, it has negative impact on the voice performance even if channel de-allocation is exploited. Although the voice blocking probability can be reduced with large slot capacity, the improvement decreases as the slot capacity increases. On the contrary, the packet loss probability increases as the slot capacity increases. In addition to the mean value analysis, the delay distribution and the 95% delay of data packets are provided.

Multiuser diversity, identified by recent information theoretic results, is a form of diversity inherent in a wireless network. The diversity gain is obtained from independent time-varying fading channels across different users. The main practical issue in multiuser diversity is lack of Quality of Service (QoS) guarantees. This study proposes a wireless scheduling algorithm named MUDSEQ for downlink channels exploiting multiuser diversity under explicit QoS constraints. The numerical results demonstrate that the novel algorithm can yield non-negligible diversity gain even under tight QoS constraints and little scattering or slow fading environments. Additionally, a system framework for dynamic resource allocation based on the proposed algorithm is developed.

Sophisticated and robust resource management is an essential issue in future wireless systems which will provide a variety of application services. In this paper, we employ an adaptive-network-based fuzzy inference system (ANFIS) to control the resource allocation for mobile multimedia networks. ANFIS, possessing the advantages of expert knowledge of fuzzy logic system and learning capability of neural networks, can provide a systematic approach to finding appropriate parameters for the Sugeno fuzzy model. The fuzzy resource allocation controller (FRAC) is designed in a two-layer architecture and selects properly the capacity requirement of new call request, the capacity reservation for future handoffs, and the air interface performance as input linguistic variables. Therefore, the statistical multiplexing gain of mobile multimedia networks can be maximized in the FRAC. Simulation results indicate that the proposed FRAC can keep the handoff call blocking rate low without jeopardizing the new call blocking rate. Also, the FRAC can indeed guarantee quality of service (QoS) contracts and achieve higher system performance according to network dynamics, compared with the guard channel scheme and ExpectedMax strategy.